Accelerated aortic 4D flow MRI with wave-CAIPI

Highly accelerated intracranial time-of-flight magnetic resonance angiography using wave-encoding

PURPOSE

To develop an accelerated 3D intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) sequence with wave-encoding (referred to as 3D wave-TOF) and to evaluate two variants: wave-controlled aliasing in parallel imaging (CAIPI) and compressed-sensing wave (CS-wave).

METHODS

A wave-TOF sequence was implemented on a 3 T clinical scanner. Wave-encoded and Cartesian k-space datasets from six healthy volunteers were retrospectively and prospectively undersampled with 2D-CAIPI sampling and variable-density Poisson disk sampling. 2D-CAIPI, wave-CAIPI, standard CS, and CS-wave schemes were compared at various acceleration factors. Flow-related artifacts in wave-TOF were investigated, and a set of practicable wave parameters was developed. Quantitative analysis of wave-TOF and traditional Cartesian TOF MRA was performed by comparing the contrast-to-background ratio between the vessel and background tissue in source images, and the structural similarity index measure (SSIM) between the maximum intensity projection images from accelerated acquisitions and their respective fully sampled references.

RESULTS

Flow-related artifacts caused by the wave-encoding gradients in wave-TOF were eliminated by properly chosen parameters. Images from wave-CAIPI and CS-wave acquisitions had a higher SNR and better-preserved contrast than traditional parallel imaging (PI) and CS methods. Maximum intensity projection images from wave-CAIPI and CS-wave acquisitions had a cleaner background, with vessels that were better depicted. Quantitative analyses indicated that wave-CAIPI had the highest contrast-to-background ratio, SSIM, and vessel-masked SSIM among the sampling schemes studied, followed by the CS-wave acquisition.

CONCLUSION

3D wave-TOF improves the capability of accelerated MRA and provides better image quality at higher acceleration factors compared to traditional PI- or CS-accelerated TOF, suggesting the potential use of wave-TOF in cerebrovascular disease.

Particle Tracing Based on 4D Flow Magnetic Resonance Imaging: A Systematic Review into Methods, Applications, and Current Developments

BACKGROUND

Particle tracing based on 4D Flow MRI has been applied as a quantitative and qualitative postprocessing technique to study temporally evolving blood flow patterns.

PURPOSE

To systematically review the various methods to perform 4D Flow MRI-based particle tracing, as well as the clinical value, clinical applications, and current developments of the technique.

STUDY TYPE

The study type is systematic review.

SUBJECTS

Patients with cardiovascular disease (such as Marfan, Fontan, Tetralogy of Fallot), healthy controls, and cardiovascular phantoms that received 4D Flow MRI with particle tracing.

FIELD STRENGTH/SEQUENCE

Three-dimensional three-directional cine phase-contrast MRI, at 1.5 T and 3 T.

ASSESSMENT

Two systematic searches were performed on the PubMed database using Boolean operators and the relevant key terms covering 4D Flow MRI and particle tracing. One systematic search was focused on particle tracing methods, whereas the other on applications. Additional articles from other sources were sought out and included after a similar inspection. Particle tracing methods, clinical applications, clinical value, and current developments were extracted.

STATISTICAL TESTS

The main results of the included studies are summarized, without additional statistical analysis.

RESULTS

Of 127 unique articles retrieved from the initial search, 56 were included (28 for methods and 54 for applications). Most articles that described particle tracing methods used an adaptive timestep, a fourth order Runge-Kutta integration method, and linear interpolation in the time dimension. Particle tracing was applied in heart chambers, aorta, venae cavae, Fontan circulation, pulmonary arteries, abdominal vasculature, peripheral arteries, carotid arteries, and cerebral vasculature. Applications were grouped as intravascular, intracardiac, flow stasis, and research.

DATA CONCLUSIONS

Particle tracing based on 4D Flow MRI gives unique insight into blood flow in several cardiovascular diseases, but the quality depends heavily on the MRI data quality. Further studies are required to evaluate the clinical value of the technique for different cardiovascular diseases.

EVIDENCE LEVEL

5.

TECHNICAL EFFICACY

Stage 1.

Pediatric magnetic resonance imaging: faster is better

Magnetic resonance imaging (MRI) has emerged as the preferred imaging modality for evaluating a wide range of pediatric medical conditions. Nevertheless, the long acquisition times associated with this technique can limit its widespread use in young children, resulting in motion-degraded or non-diagnostic studies. As a result, sedation or general anesthesia is often necessary to obtain diagnostic images, which has implications for the safety profile of MRI, the cost of the exam and the radiology department's clinical workflow. Over the last decade, several techniques have been developed to increase the speed of MRI, including parallel imaging, single-shot acquisition, controlled aliasing techniques, compressed sensing and artificial-intelligence-based reconstructions. These are advantageous because shorter examinations decrease the need for sedation and the severity of motion artifacts, increase scanner throughput, and improve system efficiency. In this review we discuss a framework for image acceleration in children that includes the synergistic use of state-of-the-art MRI hardware and optimized pulse sequences. The discussion is framed within the context of pediatric radiology and incorporates the authors' experience in deploying these techniques in routine clinical practice.

High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)

PURPOSE

The goal of this study is to leverage an advanced fast imaging technique, wave-controlled aliasing in parallel imaging (Wave-CAIPI), and a generative adversarial network (GAN) for denoising to achieve accelerated high-quality high-signal-to-noise-ratio (SNR) volumetric MRI.

METHODS

Three-dimensional (3D) T₂ -weighted fluid-attenuated inversion recovery (FLAIR) image data were acquired on 33 multiple sclerosis (MS) patients using a prototype Wave-CAIPI sequence (acceleration factor R = 3×2, 2.75 minutes) and a standard T₂ -SPACE FLAIR sequence (R = 2, 7.25 minutes). A hybrid denoising GAN entitled "HDnGAN" consisting of a 3D generator and a 2D discriminator was proposed to denoise highly accelerated Wave-CAIPI images. HDnGAN benefits from the improved image synthesis performance provided by the 3D generator and increased training samples from a limited number of patients for training the 2D discriminator. HDnGAN was trained and validated on data from 25 MS patients with the standard FLAIR images as the target and evaluated on data from 8 MS patients not seen during training. HDnGAN was compared to other denoising methods including AONLM, BM4D, MU-Net, and 3D GAN in qualitative and quantitative analysis of output images using the mean squared error (MSE) and VGG perceptual loss compared to standard FLAIR images, and a reader assessment by two neuroradiologists regarding sharpness, SNR, lesion conspicuity, and overall quality. Finally, the performance of these denoising methods was compared at higher noise levels using simulated data with added Rician noise.

RESULTS

HDnGAN effectively denoised low-SNR Wave-CAIPI images with sharpness and rich textural details, which could be adjusted by controlling the contribution of the adversarial loss to the total loss when training the generator. Quantitatively, HDnGAN (λ = 10^-3 ) achieved low MSE and the lowest VGG perceptual loss. The reader study showed that HDnGAN (λ = 10^-3 ) significantly improved the SNR of Wave-CAIPI images (P<0.001), outperformed AONLM (P = 0.015), BM4D (P<0.001), MU-Net (P<0.001) and 3D GAN (λ = 10^-3 ) (P<0.001) regarding image sharpness, and outperformed MU-Net (P<0.001) and 3D GAN (λ = 10^-3 ) (P = 0.001) regarding lesion conspicuity. The overall quality score of HDnGAN (λ = 10^-3 ) (4.25±0.43) was significantly higher than those from Wave-CAIPI (3.69±0.46, P = 0.003), BM4D (3.50±0.71, P = 0.001), MU-Net (3.25±0.75, P<0.001), and 3D GAN (λ = 10^-3 ) (3.50±0.50, P<0.001), with no significant difference compared to standard FLAIR images (4.38±0.48, P = 0.333). The advantages of HDnGAN over other methods were more obvious at higher noise levels.

CONCLUSION

HDnGAN provides robust and feasible denoising while preserving rich textural detail in empirical volumetric MRI data. Our study using empirical patient data and systematic evaluation supports the use of HDnGAN in combination with modern fast imaging techniques such as Wave-CAIPI to achieve high-fidelity fast volumetric MRI and represents an important step to the clinical translation of GANs. This article is protected by copyright. All rights reserved.